Learning of detection model using loss function

ABSTRACT

There is a desire to accurately learn a detection model. Provided is a computer-implemented method including acquiring an input image; acquiring an annotated image designating a region of interest in the input image; inputting the input image to a detection model that generates an output image showing a target region from the input image; calculating an error between the output image and the annotated image, using a loss function that weights an error inside the region of interest more heavily than an error outside the region of interest; and updating the detection model in a manner to reduce the error.

BACKGROUND

The present invention relates to learning a detection model using a lossfunction.

SUMMARY

According to an embodiment of the present invention, provided is acomputer-implemented method comprising acquiring an input image;acquiring an annotated image designating a region of interest in theinput image; inputting the input image to a detection model thatgenerates an output image showing a target region from the input image;calculating an error between the output image and the annotated image,using a loss function that weights an error inside the region ofinterest more heavily than an error outside the region of interest; andupdating the detection model in a manner to reduce the error.

Weighted cross entropy may be used as the loss function. In this way,the errors can be weighted using weighted cross entropy.

The detection model may include, between an input and an output, one ormore convolution layers, one or more pooling layers, one or moredeconvolution layers, and one or more batch normalization layers. Inthis way, in some embodiments, the detection model hastens the ofconvergence of learning, and restricts over-learning.

The computer-implemented method may further comprise acquiring at leastone coordinate in the input image; and specifying the region of interestaccording to the, at least, one coordinate. In this way, it is possibleto roughly specify the region of interest from at least one coordinate.

According to another embodiment of the present invention, provided is anapparatus comprising a processor or a programmable circuitry; and one ormore computer readable mediums collectively including instructions that,in response to being executed by the processor or the programmablecircuitry, cause the processor or the programmable circuitry to acquirean input image; acquire an annotated image designating a region ofinterest in the input image; input the input image to a detection modelthat generates an output image showing a target region from the inputimage; calculate an error between the output image and the annotatedimage, using a loss function that weights an error inside the region ofinterest more heavily than an error outside the region of interest; andupdate the detection model in a manner to reduce the error. In this way,the processor or programmable circuitry acquires an input image andaccurately learns the detection model.

According to another embodiment of the present invention, provided is acomputer program product including one or more computer readable storagemediums collectively storing program instructions that are executable bya processor or programmable circuitry to cause the processor or theprogrammable circuitry to perform operations comprising acquiring aninput image; acquiring an annotated image designating a region ofinterest in the input image; inputting the input image to a detectionmodel that generates an output image showing a target region from theinput image; calculating an error between the output image and theannotated image, using a loss function that weights an error inside theregion of interest designated by the annotated image more heavily thanan error outside the designated region of interest; and updating thedetection model in a manner to reduce the error. In this way, it ispossible to increase the accuracy associated with the machine learningof the detection model.

According to another embodiment of the present invention, provided is anapparatus comprising a processor or a programmable circuitry; and one ormore computer readable mediums collectively including instructions that,in response to being executed by the processor or the programmablecircuitry, cause the processor or the programmable circuitry to realizea neural network for generating an output image showing a target regionfrom an input image, wherein the neural network includes, between aninput and an output, a plurality of convolution layers, a plurality ofpooling layers, a plurality of deconvolution layers, and a plurality ofbatch normalization layers, and the plurality of batch normalizationlayers are arranged, respectively, after every predetermined number oflayers, in at least one of a first path including some of the pluralityof convolution layers and the plurality of pooling layers and a secondpath including the rest of the plurality of convolution layers and theplurality of deconvolution layers.

According to another embodiment of the present invention, provided is acomputer program product including one or more computer readable storagemediums collectively storing program instructions that are executable bya processor or programmable circuitry to cause the processor or theprogrammable circuitry to perform operations comprising realizing aneural network for generating an output image showing a target regionfrom an input image, wherein the neural network includes, between aninput and an output, a plurality of convolution layers, a plurality ofpooling layers, a plurality of deconvolution layers, and a plurality ofbatch normalization layers, and the plurality of batch normalizationlayers are arranged, respectively, after every predetermined number oflayers in at least one of a first path including some of the pluralityof convolution layers and the plurality of pooling layers and a secondpath including the rest of the plurality of convolution layers and theplurality of deconvolution layers. In this way, in some embodiments, thedetection model hastens the of convergence of learning, and restrictsover-learning.

The summary clause does not necessarily describe all necessary featuresof the embodiments of the present invention. The present invention mayalso be a sub-combination of the features described above. The above andother features and advantages of the present invention will become moreapparent from the following description of the embodiments taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating a computingenvironment, in which a system for learning a detection model utilizes aloss function, in accordance with an exemplary embodiment of the presentinvention.

FIG. 2 illustrates an exemplary network of the detection model withinthe environment of FIG. 1, in accordance with at least one embodiment ofthe present invention.

FIG. 3 illustrates operational processes of a detection model executingon a computing device within the environment of FIG. 1, in accordancewith at least on embodiment of the present invention.

FIG. 4 shows an example of comparisons between output images accordingto the present embodiment and output images obtained through theconventional approach.

FIG. 5 illustrates a functional block diagram illustrating a computingenvironment, in which a modification to the system for learning adetection model utilizing a loss function, in accordance with at leaston embodiment of the present invention.

FIG. 6 shows an example of the specification of a region of interest bythe apparatus 100 according to the present modification.

FIG. 7 illustrates a functional block diagram illustrating a computingenvironment, which, yet another modification to the system for learninga detection model utilizing a loss function, in accordance with at leastone embodiment of the present invention.

FIG. 8 is a block diagram of components of one or more computing deviceswithin the computing environment depicted in FIG. 1, in accordance withat least on embodiment of the present invention.

DETAILED DESCRIPTION

Hereinafter, some embodiments of the present invention will bedescribed. The embodiments do not limit the invention according to theclaims, and all the combinations of the features described in theembodiments can include but are not limited to the embodiments providedby features disclosed in the specification.

FIG. 1 is a functional block diagram of apparatus 100 according to thepresent embodiment. The apparatus 100 may be a computer such as a PC(Personal Computer), a tablet computer, a smartphone, a work station, aserver computer, or a general purpose computer, a computer system inwhich a plurality of computers are connected, or any programmableelectronic device or combination of programmable electronic devicescapable of executing a system for learning a detection model utilizes aloss function. Such a computer system is also a computer, in a broaddefinition. Furthermore, the apparatus 100 may be implemented by one ormore virtual computer environments capable of being executed in acomputer system. Instead, the apparatus 100 may be a specializedcomputer designed to update a detection model or may contain specializedhardware utilized by specialized circuitry. Furthermore, if theapparatus 100 is capable of connecting to the Internet, the apparatus100 may be utilized in cloud computing.

The apparatus 100 uses a loss function to calculate the error between anannotated image and an output image generated by inputting an inputimage to a detection model and updates the detection model in a mannerto decrease this error. In doing so, the apparatus 100 according to thepresent embodiment learns the detection model by weighting errors in aregion of interest (ROI) more heavily than errors outside the region ofinterest, as the loss function. In the present embodiment, a case inwhich the apparatus 100 learns a detection model that detects diseasesuch as a lung tumor from an X-ray image of an animal is shown, as anexample. However, the detection model includes this example, but is notlimited in scope to features recognizing tissue structures, such as, forexample, tumors in animals. The apparatus 100 may learn a detectionmodel that detects various regions from various images. The apparatus100 includes an input image acquiring section 110, an annotated imageacquiring section 120, a detection model 130, an error calculatingsection 140, a weight storage section 150, and an updating section 160.

The input image acquiring section 110 retrieves a plurality of inputimages, which, for the purposes of clarity in understanding, areconsidered to be “original” images by certain embodiments. For example,in one embodiment, the input image acquiring section 110 retrieves aplurality of X-ray images of an animal. The input image acquiringsection 110 communicates with the detection model 130 such thatdetection model 130 is provided with the plurality of acquired inputimages.

The annotated image acquiring section 120 retrieves an annotated imagedesignating a region of interest in the input image, for each of theplurality of input images. For example, annotated image acquiringsection 120 may retrieve each annotated image in which a label,indicating that a region is different from other regions, is attached tothe region of interest in the input images. The annotated imageacquiring section 120 communicates to error calculating section 140 theacquired annotated images.

The detection model 130 receives the plurality of input images retrievedfrom input image acquiring section 110 and generates each output imageshowing a target region from these input images. In the presentembodiment, a multilayer neural network, which is described furtherbelow, is used as the algorithm of detection model 130. However,detection model 130 is not limited to the present embodiment. Instead,in addition to the model described below, a neural network such as CNN,FCN, SegNet, and U-Net, or any algorithm capable of detecting a targetregion such as a support vector machine (SVM) and a determination tree,may be used as detection model 130. The detection model 130 communicatesto error calculating section 140 the generated output images.

The error calculating section 140 calculates the respective errorsbetween the output images supplied from the detection model 130 and theannotated images supplied from the annotated image acquiring section120, using the loss function. When doing this, the error calculatingsection 140 uses weighted cross entropy as the loss function, to weightthe errors inside the region of interest more heavily than the errorsoutside the region of interest. In other words, as an example, the errorcalculating section 140 weighs the errors within the region designatedas a lung tumor by a doctor than the errors outside this region. This isdescribed in detail further below.

The weight storage section 150 stores, in advance, weights and suppliesthese weights to the error calculating section 140. Error calculatingsection 140 leverages the weights with a weighted cross entropy tocalculate the errors.

The error calculating section 140 applies the weights supplied from theweight storage section 150 to the weighted cross entropy, to calculatethe errors between the output images and the annotated images andsupplies these errors to the updating section 160.

The updating section 160 updates the detection model 130 in a manner toreduce the errors supplied from the error calculating section 140. Forexample, the updating section 160 updates each parameter of thedetection model in a manner to minimize the errors, using an errorbackward propagation technique. One having ordinary skill in the artwould recognize that the error backward propagation technique itself iswidely accepted as an algorithm used when learning a detection model.

FIG. 2 shows an exemplary network of the detection model 130 accordingto the present embodiment. As an example, the detection model 130includes, between an input and an output, one or more convolutionlayers, one or more pooling layers, one or more deconvolution layers,and one or more batch normalization layers. In the present drawing, asan example, the detection model 130 includes, between an input layer 210and an output layer 260, a plurality of convolution layers 220 a to 220w (referred to collectively as “convolution layers 220”), a plurality ofpooling layers 230 a to 230 e (referred to collectively as “poolinglayers 230”), a plurality of deconvolution layers 240 a to 240 e(referred to collectively as “deconvolution layers 240”), and aplurality of batch normalization layers 250 a to 250 i (referred tocollectively as “batch normalization layers 250”).

The convolution layers 220 each output a feature map by performing aconvolution operation applied to the input image, while sliding a kernel(filter) having a predetermined magnitude.

The pooling layers 230 each compress and down-sample information, inorder to deform the input image into a shape that is easier to handle.In this case, each pooling layer 230 may use max pooling to select andcompress a maximum value of each range or may use average pooling tocalculate and compress the average value of each range, for example.

The deconvolution layers 240 each expand the size by adding blanksaround and/or between each element in the feature map input thereto, andthen perform the deconvolution operation applied while sliding a kernel(filter) having a predetermined magnitude.

The batch normalization layers 250 each replace the output of each unitwith a new value normalized for each mini batch. In other words, eachbatch normalization layer 250 performs normalization such that theelements of each of a plurality of images have a normalizeddistribution.

The detection model 130 according to the present embodiment has a firstpath that is an encoding path including the plurality of convolutionlayers 220 a to 220 l and the plurality of pooling layers 230 a to 230 eand a second path that is a decoding path including the plurality ofconvoluting layers 220 m to 220 w and the plurality of deconvolutionlayers 240 a to 240 e, for example. The encoding path and the decodingpath each connect feature maps having the same dimensions.

Furthermore, in the detection model 130 according to the presentembodiment, the plurality of batch normalization layers 250 arerespectively arranged every predetermined number of layers within atleast one of the first path including some of the plurality ofconvolution layers 220 and the plurality of pooling layers 230 and thesecond path including the rest of the plurality of convolution layers220 and the plurality of deconvolution layers 240.

As an example, the plurality of batch normalization layers 250 a to 250d are respectively arranged at every predetermined number of layers inthe encoding path, and the plurality of batch normalization layers 250 eto 250 i are respectively arranged every predetermined number of layersin the decoding path. More specifically, as shown in the presentdrawing, in the encoding path, the plurality of batch normalizationlayers 250 a to 250 d are respectively arranged after the convolutionlayers 220 d, 220 f, 220 h, and 220 j that are connected to the decodingpath. Furthermore, in the decoding path, the plurality of batchnormalization layers 250 e to 250 h are respectively arranged after theconvolution layers 220 n, 220 p, 220 r, 220 t, and 220 v.

In this way, according to the detection model 130 of the presentembodiment, at least one batch normalization layer 250 is arranged in atleast one of the encoding path and the decoding path, and therefore itis possible to prevent large change of the internal variabledistribution (internal covariate shift) and hasten the convergence ofthe learning, and also to restrict over-learning. Furthermore, by usingsuch a detection model 130, the apparatus 100 according to the presentembodiment can more efficiently learn the detection model 130 whenlearning correct label classification from labels containing errors.

FIG. 3 illustrates operational processes by which the apparatus 100learns the detection model 130, according to the present embodiment.

At step 310, the apparatus 100 acquires a plurality of input images. Forexample, the input image acquiring section 110 acquires a plurality ofX-ray images of an animal. If the sizes of the images are different fromeach other, the input image acquiring section 110 may acquire imagesthat have each undergone preprocessing (e.g. pixel value normalization,cropping to a predetermined shape, and resizing to a predetermined size)as the input images. At this time, the input image acquiring section 110may acquire the plurality of input images via the Internet, via userinput, or via a memory device or the like capable of storing data, forexample. The input image acquiring section 110 supplies the detectionmodel 130 with the plurality of acquired input images.

At step 320, the apparatus 100 acquires an annotated image designatingthe region of interest in the input image, for each of the plurality ofinput images. For example, the annotated image acquiring section 120acquires each annotated image in which pixels inside a region designatedas a lung tumor by a doctor, for example, are labeled with a class c=1and the pixels outside of this region are labeled with a class c=0. Inthe above description, an example is shown in which each pixel islabeled according to two classes (e.g. c=0 and c=1) based on whether thepixel is inside or outside of the region designated as a lung tumor, butthe present embodiment is not limited to this. For example, theannotated image acquiring section 120 may acquire each annotated imagein which pixels inside a lung region and also inside a lung tumor regionare labeled with a class c=2, pixels inside a lung region but outside ofa lung tumor region are labeled with a class c=1, and pixels outside thelung region and outside the lung tumor region are labeled with a classc=0. In other words, the annotated image acquiring section 120 mayacquire each annotated image labeled according to three or more classes(e.g. c=0, c=1, and c=2.) At this time, the annotated image acquiringsection 120 may acquire each annotated image via a network, via userinput, or via a memory device or the like capable of storing data. Theannotated image acquiring section 120 supplies the error calculatingsection 140 with the acquired annotated images.

At step 330, the apparatus 100 inputs each of the plurality of inputimages acquired at step 310 into the detection model 130 that generatesthe output images of the target region from the input images. Forexample, for each of the plurality of input images, the detection model130 generates an output image in which the pixels within the regionpredicted to be the target region, i.e. the lung tumor region, in theinput image are labeled with the class c=1 and the pixels outside thisregion are labeled with the class c=0. The detection model 130 thensupplies the error calculating section 140 with each of the generatedoutput images. In the above description, an example is shown in whichthe apparatus 100 generates the output images after acquiring theannotated images, but the apparatus 100 is not limited to this. Theapparatus 100 may acquire the annotated images after generating theoutput images. In other words, step 320 may be performed after step 330.

At step 340, the apparatus 100 calculates the respective errors betweenthe output images acquired at step 330 and the annotated images acquiredat step 320, using the loss function. At this time, the errorcalculating section 140 uses cross entropy as the loss function.Generally, cross entropy is a scale defined between two probabilitydistributions, which are a probability distribution and a referencefixed probability distribution, has a minimum value when the probabilitydistribution and the reference fixed distribution are the same, and hasa value that is larger the more that the probability distributiondiffers from the reference fixed distribution. Here, when calculatingthe error between an output image and an annotated image, if this crossentropy is used without weighting any of the pixels in the input image,the error is strongly affected by pixel groups with a wide area. Whenthis happens, if there is an error contained in the annotated image, forexample, there are cases where the detection model for detecting thetarget region cannot be learned accurately, even if the detection modelis updated to minimize this error.

Therefore, in the present embodiment, the loss function used by theerror calculating section 140 to calculate the error weights errorsinside the region of interest more heavily than errors outside theregion of interest. At this time, the error calculating section 140 mayuse weighted cross entropy as the loss function. For example, the weightstorage section 150 stores in advance the weights to be applied to thisweighted cross entropy, and supplies the error calculating section 140with these weights. The error calculating section 140 applies theweights supplied from the weight storage section 150 to the weightedcross entropy, to calculate the error between the output image and theannotated image. Here, when weighting the errors inside the region ofinterest more heavily than the errors outside the region of interest, inthe weighted cross entropy, the weight in the target region may be setto be a larger value than the weight outside the target region. Instead,in the weighted cross entropy, the weight inside the region of interestmay be set to be a larger value than the weight outside the region ofinterest. This is explained using mathematical expressions.

The error calculating section 140 uses the weighted cross entropy shownby the following expression as the loss function, for example. It shouldbe noted that X is the collection of all pixels “i” in the input image,C is the collection of all classes c, W_(c) is the weight of a class c,p_(i) ^(c) is the value of the class c at the pixel “i” in the annotatedimage, and q_(i) ^(c) is the value of the class c at the pixel “i” inthe output image.

$\begin{matrix}{\mathcal{L} = {\sum\limits_{i \in X}{\sum\limits_{c \in C}{w_{c}p_{i}^{c}\log\; q_{i}^{c}}}}} & {{Expression}\mspace{14mu} 1}\end{matrix}$

Here, an example is described in which the annotated image acquiringsection 120 acquires the annotated image in which the pixels inside theregion of interest (ROI) designated as a lung tumor by a doctor arelabeled as class c=1 and the pixels outside the region of interest arelabeled as class c=0, and the detection model 130 generates an outputimage in which the class c=1 is given to pixels within the target regionthat is predicted to be the lung tumor region in the input image and theclass c=0 is given to pixels outside of the target region.

In this case, Expression 1 is expanded into the expression shown below.Specifically, the cross entropy is expanded as the sum of fourcomponents, which are (1) a component in which the pixels inside theregion of interest are labeled with c=1 and are predicted to be insidethe target region, (2) a component in which the pixels inside the regionof interest are labeled with c=0 and are predicted to be outside thetarget region, (3) a component in which the pixels outside the region ofinterest are labeled with c=1 and are predicted to be inside the targetregion, and (4) a component in which the pixels outside the region ofinterest are labeled with c=0 and are predicted to be outside the targetregion.

$\begin{matrix}{\mathcal{L} = {{w_{c}p_{i = {ROIin}}^{c = 1}\log\; q_{i = {ROIin}}^{c = 1}} + {w_{c}p_{i = {ROIin}}^{c = 0}\log\; q_{i = {ROIin}}^{c = 0}} + {w_{c}p_{i = {ROIout}}^{c = 1}\log\; q_{i = {ROIout}}^{c = 1}} + {w_{c}p_{i = {ROIout}}^{c = o}\log\; q_{i = {ROIout}}^{c = o}}}} & {{Expression}\mspace{14mu} 2}\end{matrix}$

However, in the case described above, the probability of a pixel insidethe region of interest being labeled with c=0 is 0, and therefore thesecond term component in Expression 2 is 0. Similarly, the probabilityof a pixel outside the region of interest being labeled with c=1 is 0,and therefore the third term component in Expression 2 is 0. Theprobability of a pixel inside the region of interest being labeled withc=1 and the probability of a pixel outside the region of interest beinglabeled with c=0 are both 1, and therefore Expression 2 can be shows asthe expression below. Specifically, the cross entropy is shown as thesum of two components, which are a component in which the pixels insidethe region of interest are predicted to be inside the target region anda component in which the pixels outside the region of interest arepredicted to be outside the target region.

=w _(c)logq _(i=ROIin) ^(c=1) +w _(c)logq _(i=ROIout) ^(c=0)  Expression3

In the present embodiment, the loss function weights the errors insidethe region of interest more heavily than the errors outside the regionof interest. In other words, in Expression 3, the weight Wc of the firstterm component is set to be a larger value than the weight Wc of thesecond term component. Here, in the weighted cross entropy, by settingthe weight inside the target region to be a larger value than the weightoutside the target region, the errors inside the region of interest maybe weighted more heavily than the errors outside the region of interest.In other words, by setting the weights of the first term component andthe third term component in Expression 2 to be larger values than theweights of the second term component and the fourth term component inExpression 2, the weight of the first component in Expression 3 may beset to be a larger value than the weight of the second component inExpression 3. Instead, in the weighted cross entropy, by setting theweight inside the region of interest to be a larger value than theweight outside the region of interest, the error inside the region ofinterest may be weighted more heavily than the error outside the regionof interest. In other words, by setting the weights of the first termcomponent and the second term component in Expression 2 to be largervalues than the weights of the third term component and the fourth termcomponent in Expression 2, the weight of the first term component inExpression 3 may be set to be a larger value than the weight of thesecond term component in Expression 3. The error calculating section 140then supplies the updating section 160 with the error between the outputimage and the annotated image calculated in this manner.

At step 350, the apparatus 100 updates the detection model 130 in amanner to reduce the error calculated at step 340. For example, theupdating section 160 uses the error backward propagation technique toupdate each parameter of the detection model 130 in a manner to minimizethis error, and then ends the process.

The apparatus 100 can repeatedly perform the processes from step 330 tostep 350, in accordance with various learning methods such as batchlearning, mini batch learning, and online learning.

FIG. 4 shows an example of comparisons between output images accordingto the present embodiment and output images obtained through theconventional approach. In the present drawing, input images, outputimages obtained through the conventional approach, output imagesobtained according to the present embodiment, and overlay images inwhich the output images obtained according to the present embodiment areoverlaid on the input images are shown in the stated order, from left toright. As shown in the present drawing, with the detection modelaccording to the conventional approach, it was impossible to divide theinput images into classes and make predictions between the lung tumorregions and other region. In contrast to this, with the detection model130 learned by the apparatus 100 according to the present embodiment, itwas possible to obtain output in which lung tumor regions (regions shownin white in the drawing) and the other regions (regions shown in blackin the drawing) are divided according to class. When viewing the imagesin which this output is overlaid on the input images, the target regionspredicted by the detection model 130 mostly match the actual lung tumorregions, and it is possible to confirm the reliability of the detectionmodel 130.

In this way, according to the apparatus 100 of the present embodiment,when using the loss function to calculate the error between an outputimage and an annotated image, the loss function weights the errorsinside the region of interest more heavily than the errors outside theregion of interest. Due to this, the apparatus 100 can (i) enhance theeffect caused by the pixels inside the region of interest and (ii)calculate the error between the output image and the annotated image.The detection model is then updated in a manner to minimize the errorcalculated in this way, and therefore, even if errors are contained inthe annotated image, for example, the apparatus 100 can accurately learnthe detection model 130 for detecting the target region. In other words,the apparatus 100 can learn the detection model 130 by weak supervisedlearning that learns the correct label classification from labelsincluding errors.

FIG. 5 illustrates a functional block diagram of the apparatus 100according to a modification of the present embodiment. In the presentdrawing, components having the same function and configuration ascomponents in FIG. 1 are given the same reference numerals, and thedescription below includes only differing points. The apparatus 100according to the present modification further includes a coordinateacquiring section 510 and a region of interest specifying section 520.

The coordinate acquiring section 510 acquires at least one coordinate inthe input image. The coordinate acquiring section 510 supplies theregion of interest specifying section 520 with the at least one acquiredcoordinate. Here, the coordinate acquiring section 510 may acquire thecoordinate of only a single point in the input image or may acquire aset of a plurality of coordinates in the input image.

The region of interest specifying section 520 specifies the region ofinterest according to the, at least one, coordinate supplied from thecoordinate acquiring section 510. Here, the region of interestspecifying section 520 may specify a predetermined range, using the atleast one coordinate as a reference, as the region of interest. Insteadof or in addition to this, the region of interest specifying section 520may specify a range having a texture that is similar to the texture atthe, at least one, coordinate as the region of interest. Instead of, orin addition to this, the region of interest specifying section 520 mayspecify a region surrounded by a set of a plurality of coordinates asthe region of interest. The region of interest specifying section 520supplies the annotated image acquiring section 120 with informationconcerning the specified region of interest. The annotated imageacquiring section 120 retrieve the annotated image in accordance withthe region of interest specified by the region of interest specifyingsection 520.

FIG. 6 shows an example of the specification of a region of interest bythe apparatus 100 according to the present modification. For example,the apparatus 100 displays an input image in a screen, and receivesinput caused by manipulation by a user (for example, but not beinglimited to, a physician, a veterinarian, a laboratory technician, or astudent). The coordinate acquiring section 510, based on the user input,retrieves at least one coordinate in the input image, e.g. thecoordinate at the location indicated by the cross mark as shown in theleft image of the present drawing. The region of interest specifyingsection 520 then specifies the region of interest to be a predeterminedrange with, the at least one, coordinate as a reference, e.g. the range(region shown in white in the drawing) shaped as an ellipse with apredetermined size centered on the coordinate at the location indicatedby the cross mark such as shown in the right image in the presentdrawing.

However, the method for specifying the region of interest is not limitedto this. As described above, the region of interest specifying section520 may specify a range having a texture similar to the texture at thelocation indicated by the cross mark, for example, as the region ofinterest. As another example, a user may perform a manipulation such assurrounding a partial region in an input image using a mousemanipulation or the like, and the coordinate acquiring section 510 mayacquire a set of a plurality of coordinates surrounding this region. Theregion of interest specifying section 520 may then specify the regionsurrounded by this set of the plurality of coordinates as the region ofinterest. Furthermore, the region of interest specifying section 520 mayuse a combination of the plurality of specification methods describedabove. In other words, the region of interest specifying section 520 mayspecify a range having a texture similar to the texture of at least onecoordinate, within a predetermined range using at least one coordinateas a reference, as the region of interest. Alternatively, the region ofinterest specifying section 520 may specify a range including a similartexture within a region surrounded by a set of a plurality ofcoordinates as the region of interest.

In this way, the apparatus 100 according to the present modificationroughly specifies the region of interest by having the user input atleast one coordinate in the input image. Then, according to theapparatus 100 of the present modification, even when the annotated imagein accordance with the region of interest roughly specified in thismanner, i.e. an annotated image containing an error, is acquired, thecorrect label classification is learned from the labels containingerrors, and therefore it is possible to accurately learn the detectionmodel 130 for detecting the target region.

FIG. 7 shows an exemplary block diagram of the apparatus 100 accordingto another modification of the present embodiment. In the presentdrawing, components having the same function and configuration ascomponents in FIG. 5 are given the same reference numerals, and thedescription below includes only differing points. The apparatus 100according to the present modification further includes a region ofinterest display section 710 and a modifying section 720.

The region of interest display section 710 displays the region ofinterest specified by the region of interest specifying section 520 in amonitor or the like, for example.

The modifying section 720, while the region of interest display section710 is displaying the region of interest, receives user input andmodifies the region of interest specified by the region of interestspecifying section 520.

In this way, the apparatus 100 according to the present modification hasa function to display the region of interest roughly specified by theregion of interest specifying section 520 and to have the user modifythis region of interest. In this way, according to the apparatus 100 ofthe present modification, it is possible to reflect a modification basedon the experience of the user in the mechanically specified region ofinterest, and to acquire an annotated image with less error.

As an example, the embodiments described above can be modified in thefollowing manner. For example, the apparatus 100 includes a plurality ofdetection models, and retrieves a plurality of annotated images in whichthe regions of interest for the same input image are set to havedifferent ranges. The apparatus 100 may then learn each of the pluralityof detection models using the plurality of annotated images and use aregion where the target regions output by each detection model overlapas the target region output by the detection model 130. For example, theapparatus 100 includes two detection models and retrieves, for the sameinput image, an annotated image in which the region of interest is setto have a relatively wide range and an annotated image in which theregion of interest is set to have a relatively narrow range. Theapparatus 100 then uses the annotated image in which the region ofinterest is set to have a relatively wide range to learn one of thedetection models, and uses the annotated image in which the region ofinterest is set to have a relatively narrow range to learn the otherdetection model. A region predicted as the target region by both of thetwo detection models may then be used as the target region output by thedetection model 130.

As another example, the embodiments described above can be modified inthe following manner. For example, if the region of interest specifyingsection 520 specifies the region of interest according to at least onecoordinate, the annotated image acquiring section 120 may set theannotated image corresponding to the at least one coordinate. The abovedescription is an example of a case where the annotated image acquiringsection 120 acquires an annotated image in which pixels inside theregion of interest are labeled with the class c=1 and the pixels outsidethe region of interest are labeled with the class c=0. In other words,the annotated image acquiring section 120 acquires an annotated image inwhich all of the pixels inside the region of interest are labeled withthe same class. However, the annotated image acquiring section 120 mayacquire an annotated image in which the pixels inside the region ofinterest are labeled with classes corresponding to the positions ofthese pixels. In other words, as an example, if the region of interestspecifying section 520 has specified a region of interest centered on atleast one coordinate, the annotated image acquiring section 120 mayacquire an annotated image in which the pixel at the at least onecoordinate is labeled with the class c=1, and other pixels are labeledwith a class closer to 0 the farther these pixels are from the at leastone coordinate. In this way, the apparatus 100 can further enhance theeffect of pixels near the position designated by a user, even within theregion of interest, to learn the detection model.

Various embodiments of the present invention may be described withreference to flowcharts and block diagrams whose blocks may represent(1) steps of processes in which operations are performed, and (2)sections of apparatuses responsible for performing operations. Certainsteps and sections may be implemented by dedicated circuitry,programmable circuitry supplied with computer-readable instructionsstored on computer-readable media, and/or processors supplied withcomputer-readable instructions stored on computer-readable media.Dedicated circuitry may include digital and/or analog hardware circuitsand may include integrated circuits (IC) and/or discrete circuits.Programmable circuitry may include reconfigurable hardware circuitscomprising logical AND, OR, XOR, NAND, NOR, and other logicaloperations, flip-flops, registers, memory elements, etc., such asfield-programmable gate arrays (FPGA), programmable logic arrays (PLA),etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to individualize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the embodiments of the present invention have been described, thetechnical scope of the invention is not limited to the above describedembodiments. It is apparent to persons skilled in the art that variousalterations and improvements can be added to the above-describedembodiments. It is also apparent from the scope of the claims that theembodiments added with such alterations or improvements can be includedin the technical scope of the invention.

The operations, procedures, steps, and stages of each process performedby an apparatus, system, program, and method shown in the claims,embodiments, or diagrams can be performed in any order as long as theorder is not indicated by “prior to,” “before,” or the like and as longas the output from a previous process is not used in a later process.Even if the process flow is described using phrases such as “first” or“next” in the claims, embodiments, or diagrams, it does not necessarilymean that the process must be performed in this order.

FIG. 8 shows an exemplary hardware configuration of a computerconfigured to perform the foregoing operations, according to anembodiment of the present invention. A program that is installed in thecomputer 700 can cause the computer 700 to function as or performoperations associated with apparatuses of the embodiments of the presentinvention or one or more sections (including modules, components,elements, etc.) thereof, and/or cause the computer 700 to performprocesses of the embodiments of the present invention or steps thereof.Such a program may be executed by the CPU 700-12 to cause the computer700 to perform certain operations associated with some or all of theblocks of flowcharts and block diagrams described herein.

The computer 700 according to the present embodiment includes a CPU700-12, a RAM 700-14, a graphics controller 700-16, and a display device700-18, which are mutually connected by a host controller 700-10. Thecomputer 700 also includes input/output units such as a communicationinterface 700-22, a hard disk drive 700-24, a DVD drive 700-26 and an ICcard drive, which are connected to the host controller 700-10 via aninput/output controller 700-20. The computer also includes legacyinput/output units such as a ROM 700-30 and a keyboard 700-42, which areconnected to the input/output controller 700-20 through an input/outputchip 700-40.

The CPU 700-12 operates according to programs stored in the ROM 700-30and the RAM 700-14, thereby controlling each unit. The graphicscontroller 700-16 obtains image data generated by the CPU 700-12 on aframe buffer or the like provided in the RAM 700-14 or in itself andcauses the image data to be displayed on the display device 700-18.

The communication interface 700-22 communicates with other electronicdevices via a network 700-50. The hard disk drive 700-24 stores programsand data used by the CPU 700-12 within the computer 700. The DVD drive700-26 reads the programs or the data from the DVD-ROM 700-01 andprovides the hard disk drive 700-24 with the programs or the data viathe RAM 700-14. The IC card drive reads programs and data from an ICcard, and/or writes programs and data into the IC card.

The ROM 700-30 stores therein a boot program or the like executed by thecomputer 700 at the time of activation, and/or a program depending onthe hardware of the computer 700. The input/output chip 700-40 may alsoconnect various input/output units via a parallel port, a serial port, akeyboard port, a mouse port, and the like to the input/output controller700-20.

A program is provided by computer readable media such as the DVD-ROM700-01 or the IC card. The program is read from the computer readablemedia, installed into the hard disk drive 700-24, RAM 700-14, or ROM700-30, which are also examples of computer readable media, and executedby the CPU 700-12. The information processing described in theseprograms is read into the computer 700, resulting in cooperation betweena program and the above-mentioned various types of hardware resources.An apparatus or method may be constituted by realizing the operation orprocessing of information in accordance with the usage of the computer700-50 to a reception buffering region or the like provide on therecording medium.

For example, when communication is performed between the computer 700and an external device, the CPU 700-12 may execute a communicationprogram loaded onto the RAM 700-14 to instruct communication processingto the communication interface 700-22, based on the processing describedin the communication program. The communication interface 700-22, undercontrol of the CPU 700-12, reads transmission data stored on atransmission buffering region provided in a recording medium such as theRAM 700-14, the hard disk drive 700-24, the DVD-ROM 700-01, or the ICcard, and transmits the read transmission data to network 700-50 orwrites reception data received from network 700-50 to a receptionbuffering region or the like provided on the recording medium.

In addition, the CPU 700-12 may cause all or a necessary portion of afile or a database to be read into the RAM 700-14, the file or thedatabase having been stored in an external recording medium such as thehard disk drive 700-24, the DVD- drive 700-26 (DVD-ROM 700-01), the ICcard, etc., and perform various types of processing on the data on theRAM 700-14. The CPU 700-12 may then write back the processed data to theexternal recording medium.

Various types of information, such as various types of programs, data,tables, and databases, may be stored in the recording medium to undergoinformation processing. The CPU 700-12 may perform various types ofprocessing on the data read from the RAM 700-14, which includes varioustypes of operations, processing of information, condition judging,conditional branch, unconditional branch, search/replace of information,etc., as described throughout this disclosure and designated by aninstruction sequence of programs, and writes the result back to the RAM700-14. In addition, the CPU 700-12 may search for information in afile, a database, etc., in the recording medium. For example, when aplurality of entries, each having an attribute value of a firstattribute is associated with an attribute value of a second attribute,are stored in the recording medium, the CPU 700-12 may search for anentry matching the condition whose attribute value of the firstattribute is designated, from among the plurality of entries, and readsthe attribute value of the second attribute stored in the entry, therebyobtaining the attribute value of the second attribute associated withthe first attribute satisfying the predetermined condition.

The above-explained program or software modules may be stored in thecomputer readable media on or near the computer 700. In addition, arecording medium such as a hard disk or a RAM provided in a serversystem connected to a dedicated communication network or the Internetcan be used as the computer readable media, thereby providing theprogram to the computer 700 via the network.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to individualize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the embodiments of the present invention have been described, thetechnical scope of the invention is not limited to the above describedembodiments. It is apparent to persons skilled in the art that variousalterations and improvements can be added to the above-describedembodiments. It is also apparent from the scope of the claims that theembodiments added with such alterations or improvements can be includedin the technical scope of the invention.

The operations, procedures, steps, and stages of each process performedby an apparatus, system, program, and method shown in the claims,embodiments, or diagrams can be performed in any order as long as theorder is not indicated by “prior to,” “before,” or the like and as longas the output from a previous process is not used in a later process.Even if the process flow is described using phrases such as “first” or“next” in the claims, embodiments, or diagrams, it does not necessarilymean that the process must be performed in this order.

What is claimed is:
 1. A computer-implemented method comprising:acquiring an input image; acquiring an annotated image designating aregion of interest in the input image, wherein pixels of the annotatedimage are labelled with respective values of a class associated with theregion of interest; inputting the input image to a detection model thatgenerates an output image showing a target region from the input image,wherein pixels of the output image are labelled with respective valuesof the class based on the target region; calculating an error betweenthe output image and the annotated image, using a weighted cross entropyloss function that includes multiplying a value of a label of a pixel ofthe annotated image by a logarithm of a value of a label of acorresponding pixel of the output image, and applying a weightassociated with the class; and updating the detection model in a mannerto reduce the error.
 2. The computer-implemented method of claim 1,wherein the weighted cross entropy loss function is shown by a followingexpression, in which X is a collection of all pixels i in the inputimage, C is a collection of all classes c, W_(c) is a weight of theclass c, p_(i) ^(c) is a value of the class c at the pixel i in theannotated image, and q_(i) ^(c) is the value of the class c at the pixeli in the output image; $\begin{matrix}{\mathcal{L} = {\sum\limits_{i \in X}{\sum\limits_{c \in C}{w_{c}p_{i}^{c}\log{q_{i}^{c}.}}}}} & {{expression}\mspace{14mu} 1}\end{matrix}$
 3. The computer-implemented method of claim 1, wherein thedetection model includes, between an input and an output, one or aplurality of convolution layers, one or a plurality of pooling layers,one or a plurality of deconvolution layers, and one or a plurality ofbatch normalization layers.
 4. The computer-implemented method of claim3, wherein the plurality of batch normalization layers is arrangedrespectively every predetermined number of layers in at least one of afirst path including some of the plurality of convolution layers and theplurality of pooling layers and a second path including the rest of theplurality of convolution layers and the plurality of deconvolutionlayers.
 5. The computer-implemented method of claim 1, furthercomprising: acquiring at least one coordinate in the input image; andspecifying the region of interest according to the at least onecoordinate.
 6. The computer-implemented method of claim 5, wherein thespecifying the region of interest according to the at least onecoordinate includes specifying a predetermined range, with the at leastone coordinate as a reference, as the region of interest.
 7. Thecomputer-implemented method of claim 5, wherein the specifying theregion of interest according to the at least one coordinate includesspecifying a range having a texture similar to a texture at the at leastone coordinate as the region of interest.
 8. The computer-implementedmethod of claim 5, wherein the specifying the region of interestaccording to the at least one coordinate includes specifying a rangesurrounded by a set of a plurality of coordinates as the region ofinterest.
 9. The computer-implemented method of claim 5, furthercomprising: displaying the specified region of interest; and while theregion of interest is being displayed, receiving user input andmodifying the region of interest.
 10. A computer-implemented methodcomprising: acquiring an input image and an indication of at least onecoordinate in the input image; specifying a predetermined range of theinput image as a region of interest in the input image, resulting in anannotated image, using the at least one coordinate as a reference;inputting the input image to a detection model that generates an outputimage showing a target region from the input image; calculating an errorbetween the output image and the annotated image, using a weighted lossfunction that weights an error inside the region of interest moreheavily than an error outside the region of interest; and updating thedetection model in a manner to reduce the error between the output imageand the annotated image.
 11. The computer-implemented method of claim10, wherein the weighted loss function is shown by a followingexpression, in which X is a collection of all pixels i in the inputimage, C is a collection of all classes c, W_(c) is a weight of theclass c, p_(i) ^(c) is a value of the class c at the pixel i in theannotated image, and q_(i) ^(c) is the value of the class c at the pixeli in the output image; $\begin{matrix}{\mathcal{L} = {\sum\limits_{i \in X}{\sum\limits_{c \in C}{w_{c}p_{i}^{c}\log{q_{i}^{c}.}}}}} & {{expression}\mspace{14mu} 1}\end{matrix}$
 12. The computer-implemented method of claim 10, whereinthe detection model includes, between an input and an output, one or aplurality of convolution layers, one or a plurality of pooling layers,one or a plurality of deconvolution layers, and one or a plurality ofbatch normalization layers.
 13. The computer-implemented method of claim12, wherein the plurality of batch normalization layers is arrangedrespectively every predetermined number of layers in at least one of afirst path including some of the plurality of convolution layers and theplurality of pooling layers and a second path including the rest of theplurality of convolution layers and the plurality of deconvolutionlayers.
 14. A computer-implemented method comprising: acquiring an inputimage and an indication of at least one coordinate in the input image;specifying a region of interest in the input image, resulting in anannotated image, the region of interest having a texture similar to atexture of the at least one coordinate; inputting the input image to adetection model that generates an output image showing a target regionfrom the input image; calculating an error between the output image andthe annotated image, using a weighted loss function that weights anerror inside the region of interest more heavily than an error outsidethe region of interest; and updating the detection model in a manner toreduce the error between the output image and the annotated image. 15.The computer-implemented method of claim 14, wherein the weighted lossfunction is shown by a following expression, in which X is a collectionof all pixels i in the input image, C is a collection of all classes c,W_(c) is a weight of the class c, p_(i) ^(c) is a value of the class cat the pixel i in the annotated image, and q_(i) ^(c) is the value ofthe class c at the pixel i in the output image; $\begin{matrix}{\mathcal{L} = {\sum\limits_{i \in X}{\sum\limits_{c \in C}{w_{c}p_{i}^{c}\log{q_{i}^{c}.}}}}} & {{expression}\mspace{14mu} 1}\end{matrix}$
 16. The computer-implemented method of claim 14, whereinthe detection model includes, between an input and an output, one or aplurality of convolution layers, one or a plurality of pooling layers,one or a plurality of deconvolution layers, and one or a plurality ofbatch normalization layers.
 17. The computer-implemented method of claim16, wherein the plurality of batch normalization layers is arrangedrespectively every predetermined number of layers in at least one of afirst path including some of the plurality of convolution layers and theplurality of pooling layers and a second path including the rest of theplurality of convolution layers and the plurality of deconvolutionlayers.
 18. A computer-implemented method comprising: acquiring an inputimage and an indication of at least one coordinate in the input image;specifying a region of interest in the input image according to the atleast one coordinate, resulting in an annotated image, the region ofinterest having a range surrounded by a set of a plurality ofcoordinates; inputting the input image to a detection model thatgenerates an output image showing a target region from the input image;calculating an error between the output image and the annotated image,using a weighted loss function that weights an error inside the regionof interest more heavily than an error outside the region of interest;and updating the detection model in a manner to reduce the error betweenthe output image and the annotated image.
 19. The computer-implementedmethod of claim 18, wherein the weighted loss function is shown by afollowing expression, in which X is a collection of all pixels i in theinput image, C is a collection of all classes c, W_(c) is a weight ofthe class c, p_(i) ^(c) is a value of the class c at the pixel i in theannotated image, and q_(i) ^(c) is the value of the class c at the pixeli in the output image; $\begin{matrix}{\mathcal{L} = {\sum\limits_{i \in X}{\sum\limits_{c \in C}{w_{c}p_{i}^{c}\log{q_{i}^{c}.}}}}} & {{expression}\mspace{14mu} 1}\end{matrix}$
 20. The computer-implemented method of claim 18, whereinthe detection model includes, between an input and an output, one or aplurality of convolution layers, one or a plurality of pooling layers,one or a plurality of deconvolution layers, and one or a plurality ofbatch normalization layers.
 21. The computer-implemented method of claim20, wherein the plurality of batch normalization layers is arrangedrespectively every predetermined number of layers in at least one of afirst path including some of the plurality of convolution layers and theplurality of pooling layers and a second path including the rest of theplurality of convolution layers and the plurality of deconvolutionlayers.
 22. A computer-implemented method comprising: acquiring an inputimage and an indication of at least one coordinate in the input image;specifying a region of interest in the input image according to the atleast one coordinate, resulting in an annotated image; displaying theregion of interest; while the region of interest is being displayed,receiving user input and modifying the region of interest; inputting theinput image to a detection model that generates an output image showinga target region from the input image; calculating an error between theoutput image and the annotated image, using a weighted loss functionthat weights an error inside the region of interest more heavily than anerror outside the region of interest; and updating the detection modelin a manner to reduce the error between the output image and theannotated image.
 23. The computer-implemented method of claim 22,wherein the weighted loss function is shown by a following expression,in which X is a collection of all pixels i in the input image, C is acollection of all classes c, W_(c) is a weight of the class c, p_(i)^(c) is a value of the class c at the pixel i in the annotated image,and q_(i) ^(c) is the value of the class c at the pixel i in the outputimage; $\begin{matrix}{\mathcal{L} = {\sum\limits_{i \in X}{\sum\limits_{c \in C}{w_{c}p_{i}^{c}\log{q_{i}^{c}.}}}}} & {{expression}\mspace{14mu} 1}\end{matrix}$
 24. The computer-implemented method of claim 22, whereinthe detection model includes, between an input and an output, one or aplurality of convolution layers, one or a plurality of pooling layers,one or a plurality of deconvolution layers, and one or a plurality ofbatch normalization layers.
 25. The computer-implemented method of claim24, wherein the plurality of batch normalization layers is arrangedrespectively every predetermined number of layers in at least one of afirst path including some of the plurality of convolution layers and theplurality of pooling layers and a second path including the rest of theplurality of convolution layers and the plurality of deconvolutionlayers.